{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**数据探索**\n",
    "\n",
    "利用LightGBM/XGboost实现Happy Customer Bank目标客户（贷款成功的客户）识别\n",
    "\n",
    "\n",
    "    一、 任务说明：Happy Customer Bank目标客户识别\n",
    "https://discuss.analyticsvidhya.com/t/hackathon-3-x-predict-customer-worth-for-happy-customer-bank/3802\n",
    "\n",
    "1)\t文件说明\n",
    "Train.csv：训练数据\n",
    "Test.csv：测试数据\n",
    "\n",
    "2)\t字段说明\n",
    "数据集共26个字段: 其中1-24列为输入特征，25-26列为输出特征。\n",
    "    1. ID - 唯一ID（不能用于预测）\n",
    "    2. Gender - 性别\n",
    "    3. City - 城市\n",
    "    4. Monthly_Income - 月收入（以卢比为单位）\n",
    "    5. DOB - 出生日期\n",
    "    6. Lead_Creation_Date - 潜在（贷款）创建日期\n",
    "    7. Loan_Amount_Applied - 贷款申请请求金额（印度卢比，INR）\n",
    "    8. Loan_Tenure_Applied - 贷款申请期限（单位为年）\n",
    "    9. Existing_EMI -现有贷款的EMI（EMI：电子货币机构许可证） \n",
    "    10. Employer_Name雇主名称\n",
    "    11. Salary_Account - 薪资帐户银行\n",
    "    12. Mobile_Verified - 是否移动验证（Y / N）\n",
    "    13. VAR5 - 连续型变量\n",
    "    14. VAR1-  类别型变量\n",
    "    15. Loan_Amount_Submitted - 提交的贷款金额（在看到资格后修改和选择）\n",
    "    16. Loan_Tenure_Submitted - 提交的贷款期限（单位为年，在看到资格后修改和选择）\n",
    "    17. Interest_Rate - 提交贷款金额的利率\n",
    "    18. Processing_Fee - 提交贷款的处理费（INR）\n",
    "    19. EMI_Loan_Submitted -提交的EMI贷款金额（INR）\n",
    "    20. Filled_Form - 后期报价后是否已填写申请表格\n",
    "    21. Device_Type - 进行申请的设备（浏览器/移动设备）\n",
    "    22. Var2 - 类别型变量\n",
    "    23. Source - 类别型变量\n",
    "    24. Var4 - 类别型变量\n",
    "\n",
    "输出：\n",
    "    25. LoggedIn - 是否login（只用于理解问题的变量，不能用于预测，测试集中没有）\n",
    "    26. Disbursed - 是否发放贷款（目标变量），1为发放贷款（目标客户）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. import 工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#首先 import 必要的模块\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jhony/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:2785: DtypeWarning: Columns (12,14,23) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>Monthly_Income</th>\n",
       "      <th>DOB</th>\n",
       "      <th>Lead_Creation_Date</th>\n",
       "      <th>Loan_Amount_Applied</th>\n",
       "      <th>Loan_Tenure_Applied</th>\n",
       "      <th>Existing_EMI</th>\n",
       "      <th>Employer_Name</th>\n",
       "      <th>...</th>\n",
       "      <th>Processing_Fee</th>\n",
       "      <th>EMI_Loan_Submitted</th>\n",
       "      <th>Filled_Form</th>\n",
       "      <th>Device_Type</th>\n",
       "      <th>Var2</th>\n",
       "      <th>Source</th>\n",
       "      <th>Var4</th>\n",
       "      <th>LoggedIn</th>\n",
       "      <th>Disbursed</th>\n",
       "      <th>Unnamed: 26</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>ID000002C20</td>\n",
       "      <td>Female</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>20000</td>\n",
       "      <td>23-May-78</td>\n",
       "      <td>15-May-15</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>CYBOSOL</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
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       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>ID000004E40</td>\n",
       "      <td>Male</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>35000</td>\n",
       "      <td>07-Oct-85</td>\n",
       "      <td>04-May-15</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TATA CONSULTANCY SERVICES LTD (TCS)</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6762.9</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID000007H20</td>\n",
       "      <td>Male</td>\n",
       "      <td>Panchkula</td>\n",
       "      <td>22500</td>\n",
       "      <td>10-Oct-81</td>\n",
       "      <td>19-May-15</td>\n",
       "      <td>600000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ALCHEMIST HOSPITALS LTD</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
       "      <td>B</td>\n",
       "      <td>S143</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID000008I30</td>\n",
       "      <td>Male</td>\n",
       "      <td>Saharsa</td>\n",
       "      <td>35000</td>\n",
       "      <td>30-Nov-87</td>\n",
       "      <td>09-May-15</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BIHAR GOVERNMENT</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>ID000009J40</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bengaluru</td>\n",
       "      <td>100000</td>\n",
       "      <td>17-Feb-84</td>\n",
       "      <td>20-May-15</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>GLOBAL EDGE SOFTWARE</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
       "      <td>B</td>\n",
       "      <td>S134</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 27 columns</p>\n",
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      "text/plain": [
       "            ID  Gender       City  Monthly_Income        DOB  \\\n",
       "0  ID000002C20  Female      Delhi           20000  23-May-78   \n",
       "1  ID000004E40    Male     Mumbai           35000  07-Oct-85   \n",
       "2  ID000007H20    Male  Panchkula           22500  10-Oct-81   \n",
       "3  ID000008I30    Male    Saharsa           35000  30-Nov-87   \n",
       "4  ID000009J40    Male  Bengaluru          100000  17-Feb-84   \n",
       "\n",
       "  Lead_Creation_Date  Loan_Amount_Applied  Loan_Tenure_Applied  Existing_EMI  \\\n",
       "0          15-May-15             300000.0                  5.0           0.0   \n",
       "1          04-May-15             200000.0                  2.0           0.0   \n",
       "2          19-May-15             600000.0                  4.0           0.0   \n",
       "3          09-May-15            1000000.0                  5.0           0.0   \n",
       "4          20-May-15             500000.0                  2.0       25000.0   \n",
       "\n",
       "                         Employer_Name     ...     Processing_Fee  \\\n",
       "0                              CYBOSOL     ...                NaN   \n",
       "1  TATA CONSULTANCY SERVICES LTD (TCS)     ...                NaN   \n",
       "2              ALCHEMIST HOSPITALS LTD     ...                NaN   \n",
       "3                     BIHAR GOVERNMENT     ...                NaN   \n",
       "4                 GLOBAL EDGE SOFTWARE     ...                NaN   \n",
       "\n",
       "  EMI_Loan_Submitted Filled_Form  Device_Type Var2  Source  Var4  LoggedIn  \\\n",
       "0                NaN           N  Web-browser    G    S122     1         0   \n",
       "1             6762.9           N  Web-browser    G    S122     3         0   \n",
       "2                NaN           N  Web-browser    B    S143     1         0   \n",
       "3                NaN           N  Web-browser    B    S143     3         0   \n",
       "4                NaN           N  Web-browser    B    S134     3         1   \n",
       "\n",
       "   Disbursed Unnamed: 26  \n",
       "0          0         NaN  \n",
       "1          0         NaN  \n",
       "2          0         NaN  \n",
       "3          0         NaN  \n",
       "4          0         NaN  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"Train.csv\")\n",
    "test = pd.read_csv(\"Test.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>Employer_Name</th>\n",
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       "      <td>1000.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ATUL LTD</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SHAREKHAN PVT LTD</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
       "      <td>ID000110G00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>14650</td>\n",
       "      <td>15-Aug-91</td>\n",
       "      <td>01-May-15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>MAERSK GLOBAL SERVICE CENTRES</td>\n",
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       "      <td>Chennai</td>\n",
       "      <td>23400</td>\n",
       "      <td>22-Jul-87</td>\n",
       "      <td>01-May-15</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>SCHAWK</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
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       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
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       "<p>5 rows × 25 columns</p>\n",
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      ],
      "text/plain": [
       "            ID  Gender      City  Monthly_Income        DOB  \\\n",
       "0  ID000026A10    Male  Dehradun           21500  03-Apr-87   \n",
       "1  ID000054C40    Male    Mumbai           42000  12-May-80   \n",
       "2  ID000066O10  Female    Jaipur           10000  19-Sep-89   \n",
       "3  ID000110G00  Female   Chennai           14650  15-Aug-91   \n",
       "4  ID000113J30    Male   Chennai           23400  22-Jul-87   \n",
       "\n",
       "  Lead_Creation_Date  Loan_Amount_Applied  Loan_Tenure_Applied  Existing_EMI  \\\n",
       "0          05-May-15             100000.0                  3.0           0.0   \n",
       "1          01-May-15                  0.0                  0.0           0.0   \n",
       "2          01-May-15             300000.0                  2.0           0.0   \n",
       "3          01-May-15                  0.0                  0.0           0.0   \n",
       "4          01-May-15             100000.0                  1.0        5000.0   \n",
       "\n",
       "                   Employer_Name     ...     Loan_Tenure_Submitted  \\\n",
       "0                     APTARA INC     ...                       3.0   \n",
       "1                       ATUL LTD     ...                       5.0   \n",
       "2              SHAREKHAN PVT LTD     ...                       NaN   \n",
       "3  MAERSK GLOBAL SERVICE CENTRES     ...                       NaN   \n",
       "4                         SCHAWK     ...                       2.0   \n",
       "\n",
       "  Interest_Rate Processing_Fee EMI_Loan_Submitted Filled_Form  Device_Type  \\\n",
       "0          20.0         1000.0            2649.39           N  Web-browser   \n",
       "1          24.0        13800.0           19849.90           Y       Mobile   \n",
       "2           NaN            NaN                NaN           N  Web-browser   \n",
       "3           NaN            NaN                NaN           N       Mobile   \n",
       "4           NaN            NaN                NaN           N  Web-browser   \n",
       "\n",
       "   Var2  Source  Var4 Unnamed: 24  \n",
       "0     B    S122     3         NaN  \n",
       "1     C    S133     5         NaN  \n",
       "2     B    S133     1         NaN  \n",
       "3     C    S133     1         NaN  \n",
       "4     B    S143     1         NaN  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train : (87020, 27)\n",
      "Test : (37717, 25)\n"
     ]
    }
   ],
   "source": [
    "print(\"Train :\", train.shape)\n",
    "print(\"Test :\", test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 87020 entries, 0 to 87019\n",
      "Data columns (total 27 columns):\n",
      "ID                       87020 non-null object\n",
      "Gender                   87020 non-null object\n",
      "City                     86017 non-null object\n",
      "Monthly_Income           87020 non-null int64\n",
      "DOB                      87020 non-null object\n",
      "Lead_Creation_Date       87020 non-null object\n",
      "Loan_Amount_Applied      86949 non-null float64\n",
      "Loan_Tenure_Applied      86949 non-null float64\n",
      "Existing_EMI             86949 non-null float64\n",
      "Employer_Name            86949 non-null object\n",
      "Salary_Account           75257 non-null object\n",
      "Mobile_Verified          87019 non-null object\n",
      "Var5                     87020 non-null object\n",
      "Var1                     87020 non-null object\n",
      "Loan_Amount_Submitted    52409 non-null object\n",
      "Loan_Tenure_Submitted    52407 non-null float64\n",
      "Interest_Rate            27729 non-null float64\n",
      "Processing_Fee           27420 non-null float64\n",
      "EMI_Loan_Submitted       27726 non-null float64\n",
      "Filled_Form              87015 non-null object\n",
      "Device_Type              87020 non-null object\n",
      "Var2                     87020 non-null object\n",
      "Source                   87020 non-null object\n",
      "Var4                     87020 non-null object\n",
      "LoggedIn                 87020 non-null int64\n",
      "Disbursed                87020 non-null int64\n",
      "Unnamed: 26              12 non-null float64\n",
      "dtypes: float64(8), int64(3), object(16)\n",
      "memory usage: 17.9+ MB\n"
     ]
    }
   ],
   "source": [
    "#查看数据基本信息\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID                           0\n",
      "Gender                       0\n",
      "City                      1003\n",
      "Monthly_Income               0\n",
      "DOB                          0\n",
      "Lead_Creation_Date           0\n",
      "Loan_Amount_Applied         71\n",
      "Loan_Tenure_Applied         71\n",
      "Existing_EMI                71\n",
      "Employer_Name               71\n",
      "Salary_Account           11763\n",
      "Mobile_Verified              1\n",
      "Var5                         0\n",
      "Var1                         0\n",
      "Loan_Amount_Submitted    34611\n",
      "Loan_Tenure_Submitted    34613\n",
      "Interest_Rate            59291\n",
      "Processing_Fee           59600\n",
      "EMI_Loan_Submitted       59294\n",
      "Filled_Form                  5\n",
      "Device_Type                  0\n",
      "Var2                         0\n",
      "Source                       0\n",
      "Var4                         0\n",
      "LoggedIn                     0\n",
      "Disbursed                    0\n",
      "Unnamed: 26              87008\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print (train.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "该数据集存在缺失值，缺失值被标记为NaN。其中\n",
    "Loan_Amount_Submitted    34611\n",
    "Loan_Tenure_Submitted    34613\n",
    "Interest_Rate            59291\n",
    "Processing_Fee           59600\n",
    "EMI_Loan_Submitted       59294\n",
    "缺失值很多，接近总量的一半甚至更多。\n",
    "Unnamed列无用，可以丢弃。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 37717 entries, 0 to 37716\n",
      "Data columns (total 25 columns):\n",
      "ID                       37717 non-null object\n",
      "Gender                   37717 non-null object\n",
      "City                     37319 non-null object\n",
      "Monthly_Income           37717 non-null int64\n",
      "DOB                      37717 non-null object\n",
      "Lead_Creation_Date       37717 non-null object\n",
      "Loan_Amount_Applied      37677 non-null float64\n",
      "Loan_Tenure_Applied      37677 non-null float64\n",
      "Existing_EMI             37677 non-null float64\n",
      "Employer_Name            37675 non-null object\n",
      "Salary_Account           32681 non-null object\n",
      "Mobile_Verified          37716 non-null object\n",
      "Var5                     37717 non-null object\n",
      "Var1                     37717 non-null object\n",
      "Loan_Amount_Submitted    22799 non-null object\n",
      "Loan_Tenure_Submitted    22795 non-null float64\n",
      "Interest_Rate            12110 non-null float64\n",
      "Processing_Fee           11971 non-null float64\n",
      "EMI_Loan_Submitted       12110 non-null float64\n",
      "Filled_Form              37713 non-null object\n",
      "Device_Type              37717 non-null object\n",
      "Var2                     37717 non-null object\n",
      "Source                   37717 non-null object\n",
      "Var4                     37717 non-null object\n",
      "Unnamed: 24              5 non-null float64\n",
      "dtypes: float64(8), int64(1), object(16)\n",
      "memory usage: 7.2+ MB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID                           0\n",
      "Gender                       0\n",
      "City                       398\n",
      "Monthly_Income               0\n",
      "DOB                          0\n",
      "Lead_Creation_Date           0\n",
      "Loan_Amount_Applied         40\n",
      "Loan_Tenure_Applied         40\n",
      "Existing_EMI                40\n",
      "Employer_Name               42\n",
      "Salary_Account            5036\n",
      "Mobile_Verified              1\n",
      "Var5                         0\n",
      "Var1                         0\n",
      "Loan_Amount_Submitted    14918\n",
      "Loan_Tenure_Submitted    14922\n",
      "Interest_Rate            25607\n",
      "Processing_Fee           25746\n",
      "EMI_Loan_Submitted       25607\n",
      "Filled_Form                  4\n",
      "Device_Type                  0\n",
      "Var2                         0\n",
      "Source                       0\n",
      "Var4                         0\n",
      "Unnamed: 24              37712\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print (test.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "该数据集存在缺失值，缺失值被标记为NaN。其中\n",
    "Loan_Amount_Submitted    14918\n",
    "Loan_Tenure_Submitted    14922\n",
    "Interest_Rate            25607\n",
    "Processing_Fee           25746\n",
    "EMI_Loan_Submitted       25607\n",
    "缺失值很多，接近总量的一半甚至更多。\n",
    "Unnamed列无用，可以丢弃。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Monthly_Income</th>\n",
       "      <th>Loan_Amount_Applied</th>\n",
       "      <th>Loan_Tenure_Applied</th>\n",
       "      <th>Existing_EMI</th>\n",
       "      <th>Loan_Tenure_Submitted</th>\n",
       "      <th>Interest_Rate</th>\n",
       "      <th>Processing_Fee</th>\n",
       "      <th>EMI_Loan_Submitted</th>\n",
       "      <th>LoggedIn</th>\n",
       "      <th>Disbursed</th>\n",
       "      <th>Unnamed: 26</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.702000e+04</td>\n",
       "      <td>8.694900e+04</td>\n",
       "      <td>86949.000000</td>\n",
       "      <td>8.694900e+04</td>\n",
       "      <td>5.240700e+04</td>\n",
       "      <td>27729.000000</td>\n",
       "      <td>27420.000000</td>\n",
       "      <td>27726.000000</td>\n",
       "      <td>87020.000000</td>\n",
       "      <td>87020.000000</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.884997e+04</td>\n",
       "      <td>2.302507e+05</td>\n",
       "      <td>2.131399</td>\n",
       "      <td>3.696228e+03</td>\n",
       "      <td>9.738955e+01</td>\n",
       "      <td>19.191836</td>\n",
       "      <td>5129.135512</td>\n",
       "      <td>10997.698748</td>\n",
       "      <td>0.029867</td>\n",
       "      <td>0.014629</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.177511e+06</td>\n",
       "      <td>3.542068e+05</td>\n",
       "      <td>2.014193</td>\n",
       "      <td>3.981021e+04</td>\n",
       "      <td>8.725070e+03</td>\n",
       "      <td>5.840606</td>\n",
       "      <td>4724.051308</td>\n",
       "      <td>7512.114833</td>\n",
       "      <td>0.175342</td>\n",
       "      <td>0.120062</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.250000</td>\n",
       "      <td>1176.410000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.650000e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>15.250000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>6491.280000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.500000e+04</td>\n",
       "      <td>1.000000e+05</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>9391.385000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000e+04</td>\n",
       "      <td>3.000000e+05</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.500000e+03</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>6250.000000</td>\n",
       "      <td>12919.040000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.445544e+08</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>1.500000e+06</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>144748.280000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Monthly_Income  Loan_Amount_Applied  Loan_Tenure_Applied  Existing_EMI  \\\n",
       "count    8.702000e+04         8.694900e+04         86949.000000  8.694900e+04   \n",
       "mean     5.884997e+04         2.302507e+05             2.131399  3.696228e+03   \n",
       "std      2.177511e+06         3.542068e+05             2.014193  3.981021e+04   \n",
       "min      0.000000e+00         0.000000e+00             0.000000  0.000000e+00   \n",
       "25%      1.650000e+04         0.000000e+00             0.000000  0.000000e+00   \n",
       "50%      2.500000e+04         1.000000e+05             2.000000  0.000000e+00   \n",
       "75%      4.000000e+04         3.000000e+05             4.000000  3.500000e+03   \n",
       "max      4.445544e+08         1.000000e+07            10.000000  1.000000e+07   \n",
       "\n",
       "       Loan_Tenure_Submitted  Interest_Rate  Processing_Fee  \\\n",
       "count           5.240700e+04   27729.000000    27420.000000   \n",
       "mean            9.738955e+01      19.191836     5129.135512   \n",
       "std             8.725070e+03       5.840606     4724.051308   \n",
       "min             1.000000e+00       1.000000       15.250000   \n",
       "25%             3.000000e+00      15.250000     2000.000000   \n",
       "50%             4.000000e+00      18.000000     4000.000000   \n",
       "75%             5.000000e+00      20.000000     6250.000000   \n",
       "max             1.500000e+06      37.000000    50000.000000   \n",
       "\n",
       "       EMI_Loan_Submitted      LoggedIn     Disbursed  Unnamed: 26  \n",
       "count        27726.000000  87020.000000  87020.000000         12.0  \n",
       "mean         10997.698748      0.029867      0.014629          0.0  \n",
       "std           7512.114833      0.175342      0.120062          0.0  \n",
       "min           1176.410000      0.000000      0.000000          0.0  \n",
       "25%           6491.280000      0.000000      0.000000          0.0  \n",
       "50%           9391.385000      0.000000      0.000000          0.0  \n",
       "75%          12919.040000      0.000000      0.000000          0.0  \n",
       "max         144748.280000      5.000000      1.000000          0.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看数值型特征的基本统计量\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Monthly_Income</th>\n",
       "      <th>Loan_Amount_Applied</th>\n",
       "      <th>Loan_Tenure_Applied</th>\n",
       "      <th>Existing_EMI</th>\n",
       "      <th>Loan_Tenure_Submitted</th>\n",
       "      <th>Interest_Rate</th>\n",
       "      <th>Processing_Fee</th>\n",
       "      <th>EMI_Loan_Submitted</th>\n",
       "      <th>Unnamed: 24</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.771700e+04</td>\n",
       "      <td>3.767700e+04</td>\n",
       "      <td>37677.000000</td>\n",
       "      <td>37677.000000</td>\n",
       "      <td>22795.00000</td>\n",
       "      <td>12110.000000</td>\n",
       "      <td>11971.000000</td>\n",
       "      <td>12110.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.980311e+04</td>\n",
       "      <td>2.293886e+05</td>\n",
       "      <td>2.153887</td>\n",
       "      <td>3498.142270</td>\n",
       "      <td>17.06576</td>\n",
       "      <td>19.260686</td>\n",
       "      <td>5108.620708</td>\n",
       "      <td>10943.158599</td>\n",
       "      <td>1.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.361382e+05</td>\n",
       "      <td>3.539572e+05</td>\n",
       "      <td>2.019334</td>\n",
       "      <td>9857.470897</td>\n",
       "      <td>1986.99089</td>\n",
       "      <td>5.875957</td>\n",
       "      <td>4742.199723</td>\n",
       "      <td>7360.990563</td>\n",
       "      <td>0.894427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>1176.410000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.650000e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>6184.107500</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.500000e+04</td>\n",
       "      <td>1.000000e+05</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>3840.000000</td>\n",
       "      <td>9425.760000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000e+04</td>\n",
       "      <td>3.000000e+05</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3500.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>6250.000000</td>\n",
       "      <td>12840.030000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.500000e+07</td>\n",
       "      <td>1.500000e+07</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>430000.000000</td>\n",
       "      <td>300000.00000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>89552.030000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Monthly_Income  Loan_Amount_Applied  Loan_Tenure_Applied  \\\n",
       "count    3.771700e+04         3.767700e+04         37677.000000   \n",
       "mean     3.980311e+04         2.293886e+05             2.153887   \n",
       "std      2.361382e+05         3.539572e+05             2.019334   \n",
       "min      0.000000e+00         0.000000e+00             0.000000   \n",
       "25%      1.650000e+04         0.000000e+00             0.000000   \n",
       "50%      2.500000e+04         1.000000e+05             2.000000   \n",
       "75%      4.000000e+04         3.000000e+05             4.000000   \n",
       "max      3.500000e+07         1.500000e+07            10.000000   \n",
       "\n",
       "        Existing_EMI  Loan_Tenure_Submitted  Interest_Rate  Processing_Fee  \\\n",
       "count   37677.000000            22795.00000   12110.000000    11971.000000   \n",
       "mean     3498.142270               17.06576      19.260686     5108.620708   \n",
       "std      9857.470897             1986.99089       5.875957     4742.199723   \n",
       "min         0.000000                1.00000       3.000000       17.500000   \n",
       "25%         0.000000                3.00000      15.000000     2000.000000   \n",
       "50%         0.000000                4.00000      18.000000     3840.000000   \n",
       "75%      3500.000000                5.00000      20.000000     6250.000000   \n",
       "max    430000.000000           300000.00000      37.000000    50000.000000   \n",
       "\n",
       "       EMI_Loan_Submitted  Unnamed: 24  \n",
       "count        12110.000000     5.000000  \n",
       "mean         10943.158599     1.400000  \n",
       "std           7360.990563     0.894427  \n",
       "min           1176.410000     1.000000  \n",
       "25%           6184.107500     1.000000  \n",
       "50%           9425.760000     1.000000  \n",
       "75%          12840.030000     1.000000  \n",
       "max          89552.030000     3.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3. 查看每个变量与标签之间的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Disbursed')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['Gender'])\n",
    "plt.xlabel('Gender')\n",
    "plt.ylabel('Disbursed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Disbursed')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['City'])\n",
    "plt.xlabel('City')\n",
    "plt.ylabel('Disbursed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Disbursed')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['DOB'])\n",
    "plt.xlabel('DOB')\n",
    "plt.ylabel('Disbursed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Disbursed')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['Lead_Creation_Date'])\n",
    "plt.xlabel('Lead_Creation_Date')\n",
    "plt.ylabel('Disbursed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Disbursed')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['Employer_Name'])\n",
    "plt.xlabel('Employer_Name')\n",
    "plt.ylabel('Disbursed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Disbursed')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['Salary_Account'])\n",
    "plt.xlabel('Salary_Account')\n",
    "plt.ylabel('Disbursed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Disbursed')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrlJYFLkkTJb0o6YlC2vaSZkhakB+3y+mSdLmkhZLmStqvsM2YnH+BpDGF9GGSHs/bXC5JrdoXMzPrOVrZ4roGGFmXdg5wV0QMAe7KywBHA0Py31jgKkiBDhgHHAgcAIyrBbucZ2xhu/rXMjOzXqhlgSsi7geW1iWPAibl55OA4wrp10byELCtpF2Ao4AZEbE0IpYBM4CRed3WEfFgRARwbaEsMzPrxbr6HtfOEfE8QH7cKaf3B54r5GvLae2lt5Wkm5lZL9dTOmeU3Z+K9UgvL1waK2mWpFlLlixZzyqamVlP0NWB64V8mY/8+GJObwMGFvINABZ3kD6gJL1UREyIiOERMbxfv34bvBNmZtZ9ujpwTQFqPQPHALcX0k/NvQtHAK/mS4nTgCMlbZc7ZRwJTMvrXpM0IvcmPLVQlpmZ9WJ9W1WwpBuAQ4EdJbWRegdeBNwk6XTgWeCknH0q8HFgIfAGcBpARCyVdAEwM+c7PyJqHT6+SOq5uAVwR/4zM7NermWBKyJObrDqiJK8AZzRoJyJwMSS9FnA0A2po5mZVU9P6ZxhZmbWFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrFAcuMzOrlMoHLkkjJT0taaGkc7q7PmZm1lqVDlyS+gBXAEcDewAnS9qje2tlZmatVOnABRwALIyIRRGxApgMjOrmOpmZWQtVPXD1B54rLLflNDMz66X6dncFNpBK0mKtTNJYYGxefF3S0y2qz47AS51VmC4Z01lFNavz6j+u7K1puU49/t3A9e9eVa5/q+v+31pY9jqreuBqAwYWlgcAi+szRcQEYEKrKyNpVkQMb/XrtIrr371c/+5V5fpXue7ro+qXCmcCQyTtKmlTYDQwpZvrZGZmLVTpFldErJR0JjAN6ANMjIh53VwtMzNroUoHLoCImApM7e56ZC2/HNlirn/3cv27V5XrX+W6rzNFrNWXwczMrMeq+j0uMzPbyDhwdQJJIenbheWvShrfjVVqmpL/kHR0Ie3vJd3ZSeW/Xrf8GUnfz8/HS/pPSY9KWiDpluLIJ5LuzcN5PZr/Tszp75Y0WdLvJT0paaqk3fO6f5E0T9Lc/L78TtJjkp6TdFgT9T1b0t+WpN8rqbTXlqRrJD2T6/iUpHHrdpTKj896bLuZpF/levyPunV/KRy/3fMxWyhpvqSbJO0s6VBJvyhs80Iua76kNyRdktPHS/pqyesX38+nJF0laa1zTN179KikA0vyrFGXJvf/9fz4Hkk/ayJ/2efoa5Les46vO17SMklPtLff7Wz71cLyoZJ+0eyxbPI11vlY1m1/Q36vvtxe3Zssa9X/UT7e265PnRy4OsfbwAmSduzuiqyrSNeKvwB8R9LmkrYELgTO6KIqXBoR+0TEEOBG4G5J/QrrT8nr94mIn0kScCtwb0S8LyL2AL4O7CzpIOCTwH4RsRewHDg0IvYGNgG+2UR9zgbWClxN+KeI2AfYBxgjadf1KGND7Qtsko/VjWUZJG0O/BK4KiJ2i4gPAlcB/eryDQW2B76Q82wNLGqiDpfm47AH8CHgI3Xl1r9HH2XNQQQ2WEQsjogT28vTzudoNLBOgStbCnyGkv1WGppufbR7LLuCpHcDH46IvSLi0s4sOyI+HhGvrM+2DlydYyXp5uiXO8rYE0XEE8DPga8B44BrI+L33VCPG4HpwKfbyXYY8JeI+EFhu0cj4gFgF+CliHi7sG6xpLOAHYChku6R9C5JbZKWS3pL0mRI3yBJP7T8XV43XdInJT0IDAMukrSVpEmSGgXBzfPj8lzmeZJm5m/jE/IJs/bN82JJj+RW4d/VFyTpE5IerP9CJGl7Sbflb8EPSdpL0k7AT4B98rf09zWo36fzcRou6beSHgeez5+BrYEDJc0BbgFeBRbm7V6JiCslvQs4FThb0uOSGg2xtmk+FstynT8vaSZwAzCY1AsY4BLgnNwCW67cKiQd72F52zck/Sm3/B6StHNO3zUfn5mSLigcn8GSnsjP+0i6JNd1rqR/zNkmAkOBMwvvy27AEOD63NqZpXQl4A+SZku6Ly//MNd3uqQtSIFlAHA9MAfYIpdxnqQFpM/Tc5KW5jrcLOk6SZcDpwPnFva7aEB+L3YvO5ZKVxJuVr5CoNTyv1zSbyQtKitT0v6S5kh6b1365pJ+nI/THK2+OjEd2Cl/ptb6jBa2L/08S9pCqVU7V9KN+djUtvlD7bOdP8+z83Ed2+BlVnHg6jxXAKdI2qa7K7KevkE6qR0N/P9OLHcLrb7U9yhwfgf5fwt8oLB8fWH7HUgnm9kNtp0ODMz/OFfWXhv4X6QT5QkRcRjwFnBwRGwJDAJGSdoLuCaXc0Je9wbpxPrR/JpPAb8GfhcR/1r32t/Kr9UGTI6IF3P69yNi/4gYSvqn/WRhm74RcQCplbfG5UVJxwPnAB+PiPoREb4BzMktlq+Tvmi8CHwOeCC3uBp98RhKukLwUkTsR2pt1S73jAGWRsS+pHPDDiXbv0VqGV9G+hLx7Vowzr6cj8Pz+Tg9mtNviYj98+sL+EN+j3YmBdIPAS+w+rN3NPCcpA/n43ZubvndD3w+5/kuqeW4P/CnBvs7FtgV2Dcfr+tz+tPAj4rvS0T8DJgFXE5qlR4ELAF+R/pS+lPgfcAVEbEn8ArwKeDJwusNymWvyMdqRES8j9QSvxL4ITCfFCB3IQXQq4GL6uo9kPS53RR4sOxY5isJ80nBr2YX4BDS52yNMvOx/AEwKiLqW89nAETEh4CTgUlKrfNjgd/nz9QDax3dNZV9nr8IvJGP/YXkLyMlPhsRw4DhwFn5f70hB65OEhF/Bq4FzuruuqyPiFhOOiFdV2yxdII3C5f69gHO6yB//VhRxUuFL7e3YUS8TvrHGEs64Qi4LCI+QDopXpFPsgKukfQm8EfSt9mDcjErgSfy82WkE8ivSf9QnwNWRMSFJS9fu1T4buCIfJIAOEzSw7llcziwZ2GbW/LjbFIrpOYwUuv3ExGxrOS1DgGuy/t8N7DDenxhKnvtvYH/zM//nP/qCTgC+ArwK9LYoDsX1tcub+0EbClpdE4fKukB0kn4TeAh0nt0KPByRLxDOoG/R+m+x56kwDmBFASuKanvwaQWHOTjUeKjwA8iYiVARCzN6UOA0Q3elxHAkcDjwIGkS3Q/Ac4kfZ5rAaRYlzbglNp+578bC/s9k/QFYXzOtx1wG2mIuhfrjuEHgWOAi3KALD2Wue6n1NX9toh4JyKeLClzAnBMRDxbcpyKn6mnSP8Xu5fka0/ZZ+q/k44dETEXmNtg27MkPUb6XAwkvT8NOXB1rstI33627O6KrKd38l932pf0LbKReTT+1kZE/DUi7o2IcaSWxafyqrdJ92z6AV8iBaJBEbEF6bLeVrUiisUBi/KJeBbwY+CV/E200eu/DtwLHJLzXQmcmL/J/pDVlxJrdQL4K2v+pnJRrk+jE0dTY3Q2MA/YrJ3XjkK+snszp5DuAV6Wj8sLrLlPqZCIvwB3kk5ckALPmfk4fIPUshtHOlHVOr38mHQv8mTSMXye1Gp5J1b/bqdRfRtRfZ78vpwAPNvgfRHw/4CTgEciYrOIGASMBJ4p5KuvS3G/NyN9rq4hBTyRWhw/z/vfh9XvQe01a54nfYHqX1dmo2NZ9pkqK/Mt0v9Xmc4YXLSjz1T5C0uHkr5gHJRbkXMo+UwVOXB1ovxt7ibWbLpbkyR9ivRN94Z2st0NbCapdrmodt3+I5LeL6n4Ta0P6ZsjpG/tfYGXScHrLeBlSSew5heNd1gdxP4IDJK0W17+Cemb808llf54P6cfCPye1f98LyndG2q3w0DBH0kn1msl7Vmy/n5SAKn907+UW/zN+HfSSfVjhbTtJX0IeIzVsyv8hnRcVt0rk/QVYBvSCfmdfB+kdPDV3LL9MOk4QDqmz+f9+VzxtUktLyJiMekk96+kE/UrwCeATfN+1vs1qTMF5ONRYjrwhdr7JWl70vuyIlfzTPL7Iml/UuB8DPgsqRXVT9IxSvcQ+5KOXZm3gK0K+72yuN/58QhSgGhU15pXSJc0j1bqEdjoWG7SRFnFMj8B/N8Gx7L4mdqd1Zc8N1Sx3KHAXiV5tgGWRcQbkj5AavG2y4Gr832bNFKzNefL+f7VAuAfgMMjYkmjzPmb9/HAx5S6Mc8jXX5ZDLyLdG3+SUlzSSehj+R7LluSTri/Ar5FOkEuJ7WSXyu8xFLgDkn35PW3kQLpcNI33QdJ9+Gu05rdk2v3uOaSLjHdkntM/TAv30YKek2JiKdJ//A/1dodLcaTOlfMJd3HaHoagYh4k3Rp6nP5mN9Eurz5IjCJdNnxt6TW3hLg3yTNJ7WydiGdUN9DarWeQrrvV1S7x/UE6UR/ZU7/P8DD+TXeBxyX679trkPNSlIvwz/m+r5ACgpXaO1u818CzlDq9NHoUumPgGeBuflS1KcL70u/XK9BpPtJ40nv9edIAfwB0ufkeuA+0gg9jXqcvhu4g3RfdDNWf6Zq+72U1No5irWPWZnlpPPzHaSAVXYsZzRZFrDqWB5D+bG8EuiTLz/eCHymk24ZXAW8K7/X/ww8UpLnTqBvznMBqRXeLo+cYWY9htJv2OZExNXdXRfruRy4zKxHkDSb1NL4WCd3ELJexoHLzMwqxfe4zMysUhy4zMysUhy4zMysUhy4zMysUhy4bKOkNOXJdYXlvpKWqIPpH9R4So9VU2lo/abk2FLSy/VDN+XBR/9+HcpZY0oPFaakkHS+pI+uQ1mrBqs160lKf/1vthFYThrzbYv8o9yPsXqcvnWWR31odmSMsu2XS5oOHEf6kS45iB1C+6PlryKpb7EeWj0lRenoFmZV5RaXbczuIA2DA2l8vFVDTalk6pDCdntLultpiovP5/ylrZPckpqoNA3FHDWeBoT8+qMLy8cDd+ahcErLUZp48qeSfg5Mr6vHGlNSKE17UQtqw5Sm6ZgtaZqkXQrpjylN5dJVc7KZrRMHLtuYTSaNEL45aQy1hwvr1po6pLBuL1LAOwg4T+3PmPsvwN156o3DSENDNRqE+U5gmFZP6TCa1cG0vXIOAsZExOF15ZVOSZHHuPseafDfYaSpNWoj3v8YOCsiDsKsh/KlQttoRcRcSYNJra2pdasPIY8sHxF3SypOHXJ7vrz4Zh7T8ADgUcodCRxbuC+2OWlsvLVGwI+IFZKmACdKupk0h9P0DsoBmFGYrqMZ7yfNizUjjd9KH9KgrdsA20bEfTnfdaR5scx6FAcu29hNIU0WeShrTpzY3tQh9cPNtDf8jIBP5UFzm3EDaXR0kQLkX9orJw+WurzJsot1mlffqlKaB8tD6ViP50uFtrGbCJwfEY/Xpbc3dcgopanOdyAFvPZGfZ8G/GOemgJJjeZDqrmHNIneGaw5vcu6ltOep0nTdRyUy9pE0p551PRXJR2S8zU7ZYZZl3Lgso1aRLRFxHdLVo2n8dQhj5Cmdn8IuCD35GvkAtL0KnNzp4kLOqjPO8DNpNbf/etbTgevsYLU8/DiPNXHo6T5ngBOI017UZup2KzH8SC7ZmZWKW5xmZlZpbhzhlkXk3QUcHFd8jMRcXx31Mesanyp0MzMKsWXCs3MrFIcuMzMrFIcuMzMrFIcuMzMrFIcuMzMrFL+Cw1LwZGCfSJ4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['Mobile_Verified'])\n",
    "plt.xlabel('Mobile_Verified')\n",
    "plt.ylabel('Disbursed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Disbursed')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['Var5'])\n",
    "plt.xlabel('Var5')\n",
    "plt.ylabel('Disbursed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Disbursed')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['Var1'])\n",
    "plt.xlabel('Var1')\n",
    "plt.ylabel('Disbursed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Disbursed')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAAELCAYAAACf7VJ0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAHhNJREFUeJzt3X+8VXWd7/HXOyDthxOoJx8GODgNU2H3xtgJbWzueLUEDUW92mCNoTmXug+96q1p0mYeo/3wjpWpV0UM46epSAcQKoyQ/JH9EA9JIBJ5StMjCMfwZxYGfu4f63tic9hn781h7732Puf9fDz2Y6/1Wd/1XZ+lPPiw1vru71JEYGZmVm+vyzsBMzMbmFyAzMwsFy5AZmaWCxcgMzPLhQuQmZnlwgXIzMxy4QJkZma5cAEyM7NcuACZmVkuBuedQC0cfPDBMWrUqLzTMDNrKqtXr342Ilrqdbx+WYBGjRpFe3t73mmYmTUVSb+t5/F8C87MzHLhAmRmZrlwATIzs1y4AJmZWS5cgMzMLBcuQGZmlgsXIDMzy4ULkJmZ5cIFyMzMcuECZGZmuXABMjOzXLgAmZlZLlyAzMwsFy5AZmaWCxcgMzPLhQuQmZnlwgXIzMxy4QJkZma5qHkBkjRI0sOSvpvWD5f0oKTHJN0h6fUpvl9a70jbRxX0cWmKb5Q0vtY5m5lZ7dXjCugiYEPB+leAayJiNPAccF6Knwc8FxF/DVyT2iFpDDAZOAKYANwoaVAd8jYzsxqqaQGSNAL4MPDNtC7gOKAtNZkLnJqWJ6V10vbjU/tJwPyI2B4RjwMdwLha5m1mZrVX6yuga4F/BV5L6wcBz0fEjrTeCQxPy8OBpwDS9hdS+z/Hi+zzZ5KmSmqX1N7V1VXt8zAzsyqrWQGSNBHYGhGrC8NFmkaZbaX22RWImBERrRHR2tLSstf5mplZfQ2uYd/HAKdIOgnYH/gLsiuioZIGp6ucEcCm1L4TGAl0ShoMvAXYVhDvVriPmZk1qZpdAUXEpRExIiJGkQ0i+GFEfAy4BzgjNZsCLEnLS9M6afsPIyJSfHIaJXc4MBpYVau8zcysPmp5BdSbzwHzJX0ZeBiYmeIzgVskdZBd+UwGiIj1khYAjwI7gPMjYmf90zYzs2pSdpHRv7S2tkZ7e3veaZiZNRVJqyOitV7H80wIZmaWCxcgMzPLhQuQmZnlwgXIzMxy4QJkZma5cAEyM7NcuACZmVkuXIDMzCwXLkBmZpYLFyAzM8uFC5CZmeXCBcjMzHLhAmRmZrlwATIzs1y4AJmZWS5qVoAk7S9plaRfSFov6QspPkfS45LWpM/YFJek6yR1SFor6ciCvqZIeix9pvR2TDMzax61fCPqduC4iHhZ0hDgAUl3pW2fjYi2Hu1PJHvd9mjgKGA6cJSkA4HLgFYggNWSlkbEczXM3czMaqxmV0CReTmtDkmfUq9fnQTMS/v9DBgq6VBgPLAiIralorMCmFCrvM3MrD5q+gxI0iBJa4CtZEXkwbTpinSb7RpJ+6XYcOCpgt07U6y3uJmZNbGaFqCI2BkRY4ERwDhJ7wYuBd4JvA84EPhcaq5iXZSI70bSVEntktq7urqqkr+ZmdVOXUbBRcTzwL3AhIjYnG6zbQdmA+NSs05gZMFuI4BNJeI9jzEjIlojorWlpaUGZ2FmZtVUy1FwLZKGpuU3AB8Efpme6yBJwKnAI2mXpcDH02i4o4EXImIzsBw4QdIwScOAE1LMzMyaWC1HwR0KzJU0iKzQLYiI70r6oaQWsltra4BPpfbLgJOADuAV4FyAiNgm6UvAQ6ndFyNiWw3zNjOzOlBEqYFpzam1tTXa29vzTsPMrKlIWh0RrfU6nmdCMDOzXLgAmZlZLlyAzMwsFwOiAHVNn513CmZm1sOAKEBmZtZ4XIDMzCwXLkBmZpYLFyAzM8uFC5CZmeXCBcjMzHLhAmRmZrlwATIzs1y4AJmZWS5cgMzMLBcuQGZmlotavhF1f0mrJP1C0npJX0jxwyU9KOkxSXdIen2K75fWO9L2UQV9XZriGyWN35s8uqbPqeJZmZlZtdTyCmg7cFxEvAcYC0xIr9r+CnBNRIwGngPOS+3PA56LiL8GrkntkDQGmAwcAUwAbkxvWTUzsyZWswIUmZfT6pD0CeA4oC3F5wKnpuVJaZ20/XhJSvH5EbE9Ih4ne2X3uFrlbWZm9VHTZ0CSBklaA2wFVgC/Bp6PiB2pSScwPC0PB54CSNtfAA4qjBfZx8zMmlRNC1BE7IyIscAIsquWdxVrlr7Vy7be4ruRNFVSu6T2rq6uvqZsZmZ1UpdRcBHxPHAvcDQwVNLgtGkEsCktdwIjAdL2twDbCuNF9ik8xoyIaI2I1paWllqchpmZVVEtR8G1SBqalt8AfBDYANwDnJGaTQGWpOWlaZ20/YcRESk+OY2SOxwYDayqVd5mZlYfg8s36bNDgblpxNrrgAUR8V1JjwLzJX0ZeBiYmdrPBG6R1EF25TMZICLWS1oAPArsAM6PiJ01zNvMzOqgZgUoItYCf1sk/huKjGKLiD8CZ/bS1xXAFdXO0czM8uOZEMzMLBcuQGZmlgsXIDMzy4ULkJmZ5cIFyMzMcuECZGZmuXABMjOzXLgAmZlZLlyAzMwsFy5AZmaWi5JT8Uj6dKntEXF1ddMxM7OBotxccAek73cA7yObmRrgZOD+WiVlZmb9X8kCFBFfAJD0A+DIiHgprV8OfLvm2ZmZWb9V6TOgw4BXC9ZfBUZVPRuzAWJi2215p2CWu0pfx3ALsErSYrLXYZ8GzKtZVmZm1u9VdAWU3sdzLvAc8DxwbkT831L7SBop6R5JGyStl3RRil8u6WlJa9LnpIJ9LpXUIWmjpPEF8Qkp1iHpkr6cqJmZNZa9eSHdG4EXI2J2et324RHxeIn2O4DPRMTPJR0ArJa0Im27JiKuKmwsaQzZW1CPAN4G3C3pb9LmacCHgE7gIUlLI+LRvcjdzMwaTEUFSNJlQCvZaLjZwBDgW8Axve0TEZuBzWn5JUkbgOElDjMJmB8R24HH06u5u9+c2pHepIqk+amtC5CZWROrdBDCacApwO8BImITu4ZolyVpFNnruR9MoQskrZU0S9KwFBsOPFWwW2eK9RY3M7MmVmkBejUigmwAApLeVOkBJL0ZWAhcHBEvAtOBtwNjya6Qvt7dtMjuUSLe8zhTJbVLau/q6qo0PTMzy0mlBWiBpG8AQyX9T+Bu4OZyO0kaQlZ8bo2IRQARsSUidkbEa6mP7ttsncDIgt1HAJtKxHcTETMiojUiWltaWio8LTMzy0tFz4Ai4ipJHwJeJHsO9B8RsaLUPpIEzAQ2FE7ZI+nQ9HwIslt7j6TlpcBtkq4mG4QwGlhFdgU0WtLhwNNkAxU+WuH5mZlZg6p0EMKbgB9GxApJ7wDeIWlIRPypxG7HAGcD6yStSbHPA2dJGkt2G+0J4JMAEbFe0gKywQU7gPMjYmc6/gXAcmAQMCsi1u/leZqZWYOpdBj2/cDfpwEDdwPtwD8CH+tth4h4gOLPb5aV2OcK4Ioi8WWl9jMzs+ZT6TMgRcQrwOnA9RFxGjCmdmmZ9U8T227NOwWzhlFxAZL0frIrnu+l2N78iNXMzGw3lRagi4BLgcXpWc1fAffULi0zM+vvyl7FSBoEnBwRp3TH0qwEF9YyMTMz69/KXgGlkWjvrUMuZmY2gFT6HOdhSUvJXkL3++5g949LzczM9lalBehA4HfAcQWxAFyAzMysTyqdCeHcWidiZmYDS6UzIcymyASgEfGJqmdkZmYDQqW34L5bsLw/2Rxue0wIamZmVqlKb8EtLFyXdDvZlDxmZmZ9UukPUXsaDRxWzUTMzGxgqfQZ0EvsejlcAM8An6thXmZm1s9Veguu4tdvm5mZVaLiCUUlnQ58gOwK6EcRcWfNsjIzs36vomdAkm4EPgWsI3uD6ackTatlYmZm1r9VOgjhH4DxETE7ImYDJwHHltpB0khJ90jaIGm9pItS/EBJKyQ9lr6HpbgkXSepQ9JaSUcW9DUltX9M0pQ+namZmTWUSgvQRnYf9TYSWFtmnx3AZyLiXcDRwPmSxgCXACsjYjSwMq0DnEg2um40MBWYDlnBAi4DjgLGAZd1Fy0zM2teJQuQpO+kSUgPAjZIulfSPcAGoKXUvhGxOSJ+npZfSvsMByYBc1OzucCpaXkSMC8yPwOGSjoUGA+siIhtEfEcsAKY0IdzNTOzBlJuEMJV1TiIpFHA3wIPAodExGbIipSkt6Zmw4GnCnbrTLHe4mZm1sRKFqCIuA9A0puAP0TEa5L+BngncFclB5D0ZmAhcHFEvCip16bFUigR73mcqWS37jjsMP9G1sys0VX6DOh+YH9Jw8me25wLzCm3k6QhZMXn1oJ3B21Jt9ZI31tTvJPs2VK3EWTzzfUW301EzIiI1ohobWkpeXfQzMwaQKUFSBHxCnA6cH1EnAYcUXKH7FJnJrAhIq4u2LQU6B7JNgVYUhD/eBoNdzTwQrpVtxw4QdKwNPjghBQzM7MmVukPUSXp/cDHgPNSbFCZfY4BzgbWSVqTYp8HrgQWSDoPeBI4M21bRja8uwN4hewqi4jYJulLwEOp3RcjYluFeZuZWYOqtABdDFwKLI6I9ZL+Crin1A4R8QDFn98AHF+kfQDn99LXLGBWhbmamVkTqHQuuPuA+wrWfwNcWKukzMys/ytZgCRdGxEXS/oOxd+IekrNMjMzs36t3BXQLem7Kr8HMjMz61bud0Cr0/d9klrSclc9EjMzs/6t3FQ8knS5pGeBXwK/ktQl6T/qk56ZmfVX5X4HdDHZcOr3RcRBETGMbFLQYyT9n5pnZ2Zm/Va5AvRx4KyIeLw7kEbA/VPaZmZm1iflCtCQiHi2ZzA9BxpSm5TMzGwgKFeAXu3jNjMzs5LKDcN+j6QXi8QF7F+DfMzMbIAoNwy73HxvZmZmfVLpbNhmZmZV5QJkZma5cAEyM7NcuACZmVkualaAJM2StFXSIwWxyyU9LWlN+pxUsO1SSR2SNkoaXxCfkGIdki6pVb5mZlZftbwCmgNMKBK/JiLGps8yAEljgMlkr/meANwoaZCkQcA04ERgDHBWamtmZk2u0jei7rWIuF/SqAqbTwLmR8R24HFJHcC4tK0jTf+DpPmp7aNVTtfMzOosj2dAF0ham27RDUux4cBTBW06U6y3uJmZNbl6F6DpwNuBscBm4OspriJto0R8D5KmSmqX1N7V5VcWmZk1uroWoIjYEhE7I+I14GZ23WbrBEYWNB0BbCoRL9b3jIhojYjWlpaW6idvZmZVVdcCJOnQgtXTgO4RckuByZL2k3Q4MBpYBTwEjJZ0uKTXkw1UWFrPnM3MrDZqNghB0u3AscDBkjqBy4BjJY0lu432BPBJgIhYL2kB2eCCHcD5EbEz9XMBsBwYBMyKiPW1ytnMzOqnlqPgzioSnlmi/RXAFUXiy4BlVUzNzMwagGdCMDOzXLgAmZlZLlyAzMwsFy5AZmaWCxcgMzPLhQuQmZnlwgXIzMxy4QJkZma5cAEyM7NcuACZmVkuXIDMzCwXLkBmZpYLFyAzM8uFC5CZmeXCBcjMzHJRswIkaZakrZIeKYgdKGmFpMfS97AUl6TrJHVIWivpyIJ9pqT2j0maUqt8zcysvmp5BTQHmNAjdgmwMiJGAyvTOsCJZK/hHg1MBaZDVrDI3qR6FDAOuKy7aJmZWXOrWQGKiPuBbT3Ck4C5aXkucGpBfF5kfgYMlXQoMB5YERHbIuI5YAV7FjUzM2tC9X4GdEhEbAZI329N8eHAUwXtOlOst7iZmTW5RhmEoCKxKBHfswNpqqR2Se1dXV1VTc7MzKqv3gVoS7q1RvremuKdwMiCdiOATSXie4iIGRHRGhGtLS0tVU/czMyqq94FaCnQPZJtCrCkIP7xNBruaOCFdItuOXCCpGFp8MEJKWZmZk1ucK06lnQ7cCxwsKROstFsVwILJJ0HPAmcmZovA04COoBXgHMBImKbpC8BD6V2X4yIngMbzMysCdWsAEXEWb1sOr5I2wDO76WfWcCsKqZmZmYNoFEGIZiZ2QDjAmRmZrlwATIzs1y4AAFbb7o27xTMzAYcFyAzM8uFC5CZmeXCBcjMzHLhAmRmZrlwAUq23nR13imYmQ0oLkBmZpYLFyCznExsuz3vFMxy5QJkZma5cAEyM7NcuACZmVkuXIB62DL9q3mnYGY2ILgAmZlZLnIpQJKekLRO0hpJ7Sl2oKQVkh5L38NSXJKuk9Qhaa2kI/PI2czMqivPK6D/HhFjI6I1rV8CrIyI0cDKtA5wIjA6faYC0+ueqZmZVV0j3YKbBMxNy3OBUwvi8yLzM2CopEPLddY1/ZbaZAk8Pe2CqvW16hsnV60vM7NmklcBCuAHklZLmppih0TEZoD0/dYUHw48VbBvZ4qZmVkTy6sAHRMRR5LdXjtf0n8r0VZFYrFHI2mqpHZJ7V1dXdXKs2Gs/OaH807BzKyqcilAEbEpfW8FFgPjgC3dt9bS99bUvBMYWbD7CGBTkT5nRERrRLS2tLTUMn0zM6uCuhcgSW+SdED3MnAC8AiwFJiSmk0BlqTlpcDH02i4o4EXum/VNaM100/JOwUzs4YwOIdjHgIsltR9/Nsi4vuSHgIWSDoPeBI4M7VfBpwEdACvAOfWP2UzM6u2uhegiPgN8J4i8d8BxxeJB3B+HVLbK503fJIRF3wj7zTMzJpWIw3Dth7uu9kDD6y8SW3L8k7BrE9cgAaAttkT8k7BzGwPLkBmZpYLFyAzM8uFC9Be2jTt4rxTsAHk5LZFeadQ1kcX/TbvFKxJuQDtg84bzquo3fob/dsfq61JbXflnYLZXhuQBajrpuoPn/7tdafttr5x2qSqH6PQ8pkn1bT/Qt+4ZXzdjmUDy7TFW/JOwXI0IAtQNT11/dl5p2DWq1Pb7s47hX327YXP5p2C1YgLkJmZ5cIFqIxnbry8bJsnr59c9eP+yD9C7Tcmts2ran+ntC2tan9meXEBqpN1HohgZUxsW7Db+sltC/vc16ltK3rddtrC+/vcbyP6/nzfomtWLkAN5sczJpZt84OCAQjL6jgYodD1t3pgQqPb2+c//2PhgzXKZN+1+TlQv+QCNIAs8JQ8TevktjsBOKVtSfr+Tp7pmFWFC1Adrd3HdwHd/c3yVztLZp24T8eYPfcEZs09YY/4dA/FromJbfOZ2Da/ZJuT2xbXKZvquGhxZ9k21y5+purHXXF7/3sTcn83oApQ103f3Kf9N037TJUyqa0796II3TKnuoXlq7fv6u+KO1y0GtnpCx9I3z/d577O6WU2hH9b/PQ+913ozjbfiutPmqYASZogaaOkDkmX1OIYW6Zf9eflZ6ZfwTPTv1iLw+zhwW+Uf+7TF4tqfMvtOj8H6pOJbbcVid1Rlb4ntS2vSj9nLPz5n5fPXLiWMxeu2+s+PtfH4nPLor2/klneIAMRNn/tybxTaCpNUYAkDQKmAScCY4CzJI3pS19dN91czdSq5qcFgw8euLn6BenbNSpG/++2yorQ5QtcrBrJaQt/1Kf9PrLwUT6y8JdVzqa6Vt7mW3HNoikKEDAO6IiI30TEq8B8oOxcN13T+/b7iy3T/7NP+3V77IbaTsPT0/dm7nnLbfHsLLawwsIzd86ez316uvFb45n2rfHcUMGVz3/OL9/mX9om8OmFu+c3dbEHSvSmeyBCbya1fT99/6Ae6ZT0LxU8B7q+ztPw/HRu5YXp0elZbhunbeFXN3i6oFpplgI0HHiqYL0zxSrWddOsovGtN13X96yq7CcVDMEu9P0+DMGeX1CQvpWe/8xL33MKBh/MnNf3K5YrC4rPl+8YzxfTs6DLFvReXC5YVHzbpCUTmLA0K6YnLvnIn+MnLik/EexJd/57Rfl+eHF26/XDi67mw4uuqWifYiYuLP5nbK/66PFboFo7feGP0/fP9th2xsKHq3qsKxdv5mu9DD6YsWjrbuu3pttwdxQMv15U5PnPXXfsHrs7Xf3cc2sX935rV8H58bxdyw/O2XWs1TO38vOZux+7mI7rt/Dr63ofOLH5q6VvNz7z9Y1ljzEQKSLyzqEsSWcC4yPin9P62cC4iPjfBW2mAlPT6juA3/Xo5gDgpQoPWWnbardrlj59Pv3j2LXo0+fT2H2Wa3dQRBxQSWLVMLheB9pHncDIgvURwKbCBhExA5jRvS6pvUcfLcDjFR6v0rbVbtcsffp8+sexa9Gnz6ex+yzXrq6jOZrlFtxDwGhJh0t6PTAZ8IRYZmZNrCmugCJih6QLgOXAIGBWRKzPOS0zM9sHTfEMqC/SM6FCfw9UOva00rbVbtcsffp8+sexa9Gnz6ex+yzbLj3OqIt+W4DMzKyxNcszIDMz62ca7hmQpJHAPOAo4A0pHMBrZM9/zMyssfyR7ILm9cBO4DlgG3BhRPQ6P1QjXgHtAG4E/gB0T0i1Hfg92Y9R/0hWjP6UYmZmVl8703eQDeven+yC5uy07QDgJODGNJVaUQ1XgCJiM3AgWaHpnh30VbITuhbYj6wADQaGFO5axzTNzAzErllp/kQ2V+eLZH9HvwvoIJtKraiGK0DJ6WRXQoel9QPITvTr6Xtw+i7MX/VM0MxsACv8u7f7H/87gLcDW8nuTo2hzLRpjfgMaCLwa2AJcA1ZJX0Z+AuykxlR0Lzh8jczG6CC3S8Eosf3HhrxCugY4BSyq51XyU5of7JC5PcQm5nl77WC5e6iM4TslttbgTcBGygybVqhRryC+DzZJdsfgJ8A08lGvz0LnJXadFfa7gdhr8O34MzM6i2Ap4HDyQrQBrK7VZGWRwOretu54X6IKukDZL/U3YmHXZuZNYPtZBcB3cOwnyd7I8HFEXFXbzs1XAEyM7OBoRGfAZmZ2QDgAmRmZrlwATIzs1y4AJmZWS5cgMzMLBcuQGZmlgsXIMuNpJdzOu5pkkLSO/M4fkEeF0t6Y5k2n5C0TtJaSY9ImlSm/TmSbtiHnE6RdElaPlXSmB59v20v+xsl6ZG+5mP9mwuQDURnAQ8Ak3PO42Kg1wIkaQTwb8AHIuK/AkcDa2uZUEQsjYgr0+qpZBNKdjsH2KsCZFaKC5A1FEl/KWll+hf/SkmHpfjJkh6U9LCkuyUdkuKXS5ol6V5Jv5F0YZn+30w23+B5FBQgScdKuk/SAkm/knSlpI9JWpWuQN5eJr85ks4o6O/lgn7vldQm6ZeSblXmQrK/zO+RdE8v6b4VeIlsMl4i4uWIeDz1e6+k1rR8sKQnCvYbKen7kjZKuiy1GZWO/810JXWrpA9K+rGkxySNS+3OkXSDpL8jm5Pxa5LWSPoc0ArcmtbfIOm96b/ZaknLJR2a+nivpF9I+ilwfqn/HzbARYQ//uTyAV4uEvsOMCUtfwK4My0PY9fMHf8MfD0tX042Z+B+wMFk038MKXHMfwJmpuWfAEem5WPJpg85NPX1NPCFtO0i4Noy+c0Bzuh5bqnfF8gmZXwd8FOyKxqAJ4CDS+Q6CFgOPAnMBk4u2HYv0JqWDwaeSMvnAJuBg8jeKPwIWeEYRTZd/n9JeawGZpFNnzKp4DzOAW7o5ZwKjzkk/fdrSev/CMxKy2uBf0jLXwMeyfvPmj+N+fEVkDWa9wO3peVbgA+k5RHAcknrgM8CRxTs872I2B4Rz5K9i+SQEv2fBcxPy/PZNcEtwEMRsTkitpO9EuQHKb6O7C/wUvmVsioiOiPiNWBNQV8lRcROYAJwBvAr4BpJl1ew64qI+F1E/AFYVJDj4xGxLuWxHlgZEcHu51epdwDvBlZIWgP8OzBC0luAoRFxX2p3y172awNII86GbVaoe7LC64GrI2KppGPJrny6bS9Y3kkvf64lHQQcB7xbUpBdYYSkfy3Sz2sF691v4C2V3w7SLW1J3ZMy7lV+RTvPCsQqYJWkFWRXQpcXHo/sdSXFcuq53pfz642A9RHx/t2C0tAixzcryldA1mh+wq5nMx8jGywA8Bay22IAU/rY9xnAvIj4y4gYFREjyd5nX8lVTLn8ngDem5Ynsfvr4nvzEtnbfouS9DZJRxaExgK/LXK8M9jdhyQdKOkNZAMJflxBLpXkV7i+EWiR9P6U6xBJR0TE88ALaVZ7yP4bmRXlAmR5eqOkzoLPp4ELgXMlrQXOJnv+Atm/+r8t6Udk74bqi7OAxT1iC4GP7kUfveV3M/APklYBR5G9kricGcBdJQYhDAGuSoMH1pA9Z+k+3lXA/5L0E7JnQIUeILv1tQZYGBHtlZ3aHuYDn00DP95O9kzoppTLILLC9xVJv0jH+ru037nAtDQI4Q99PLYNAH4dg5mZ5cJXQGZmlgsPQrB+Jw02WFlk0/ER8bt651MJSQ+SDf8udHZErMsjH7N68C04MzPLhW/BmZlZLlyAzMwsFy5AZmaWCxcgMzPLhQuQmZnl4v8DkOzHXs3ardAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['Loan_Amount_Submitted'])\n",
    "plt.xlabel('Loan_Amount_Submitted')\n",
    "plt.ylabel('Disbursed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Disbursed')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['Filled_Form'])\n",
    "plt.xlabel('Filled_Form')\n",
    "plt.ylabel('Disbursed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Disbursed')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['Device_Type'])\n",
    "plt.xlabel('Device_Type')\n",
    "plt.ylabel('Disbursed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Disbursed')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['Var2'])\n",
    "plt.xlabel('Var2')\n",
    "plt.ylabel('Disbursed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Disbursed')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['Source'])\n",
    "plt.xlabel('Source')\n",
    "plt.ylabel('Disbursed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Disbursed')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['Var4'])\n",
    "plt.xlabel('Var4')\n",
    "plt.ylabel('Disbursed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数字型变量与标签y的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jhony/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Disbursed')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.Monthly_Income, kde = False)\n",
    "plt.xlabel('Monthly_Income')\n",
    "plt.ylabel('Disbursed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jhony/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Disbursed', y='Monthly_Income', data=train, hue=\"Disbursed\")\n",
    "plt.xlabel('Target customer identification', fontsize=12)\n",
    "plt.ylabel('Monthly_Income', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Disbursed')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['Loan_Tenure_Applied'])\n",
    "plt.xlabel('Loan_Tenure_Applied')\n",
    "plt.ylabel('Disbursed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Disbursed')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['Loan_Amount_Applied'])\n",
    "plt.xlabel('Loan_Amount_Applied')\n",
    "plt.ylabel('Disbursed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Disbursed')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['Existing_EMI'])\n",
    "plt.xlabel('Existing_EMI')\n",
    "plt.ylabel('Disbursed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Disbursed')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['Loan_Tenure_Submitted'])\n",
    "plt.xlabel('Loan_Tenure_Submitted')\n",
    "plt.ylabel('Disbursed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Disbursed')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['Interest_Rate'])\n",
    "plt.xlabel('Interest_Rate')\n",
    "plt.ylabel('Disbursed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Disbursed')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['Processing_Fee'])\n",
    "plt.xlabel('Processing_Fee')\n",
    "plt.ylabel('Disbursed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Disbursed')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['EMI_Loan_Submitted'])\n",
    "plt.xlabel('EMI_Loan_Submitted')\n",
    "plt.ylabel('Disbursed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**特征之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd635a3b4e0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_corr = train.corr().abs()\n",
    "\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
